Search results for: MATLAB and simulink
270 Forecasting Performance Comparison of Autoregressive Fractional Integrated Moving Average and Jordan Recurrent Neural Network Models on the Turbidity of Stream Flows
Authors: Daniel Fulus Fom, Gau Patrick Damulak
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In this study, the Autoregressive Fractional Integrated Moving Average (ARFIMA) and Jordan Recurrent Neural Network (JRNN) models were employed to model the forecasting performance of the daily turbidity flow of White Clay Creek (WCC). The two methods were applied to the log difference series of the daily turbidity flow series of WCC. The measurements of error employed to investigate the forecasting performance of the ARFIMA and JRNN models are the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The outcome of the investigation revealed that the forecasting performance of the JRNN technique is better than the forecasting performance of the ARFIMA technique in the mean square error sense. The results of the ARFIMA and JRNN models were obtained by the simulation of the models using MATLAB version 8.03. The significance of using the log difference series rather than the difference series is that the log difference series stabilizes the turbidity flow series than the difference series on the ARFIMA and JRNN.Keywords: auto regressive, mean absolute error, neural network, root square mean error
Procedia PDF Downloads 265269 Load Forecasting Using Neural Network Integrated with Economic Dispatch Problem
Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq
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High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch
Procedia PDF Downloads 184268 Optimal Pressure Control and Burst Detection for Sustainable Water Management
Authors: G. K. Viswanadh, B. Rajasekhar, G. Venkata Ramana
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Water distribution networks play a vital role in ensuring a reliable supply of clean water to urban areas. However, they face several challenges, including pressure control, pump speed optimization, and burst event detection. This paper combines insights from two studies to address these critical issues in Water distribution networks, focusing on the specific context of Kapra Municipality, India. The first part of this research concentrates on optimizing pressure control and pump speed in complex Water distribution networks. It utilizes the EPANET- MATLAB Toolkit to integrate EPANET functionalities into the MATLAB environment, offering a comprehensive approach to network analysis. By optimizing Pressure Reduce Valves (PRVs) and variable speed pumps (VSPs), this study achieves remarkable results. In the Benchmark Water Distribution System (WDS), the proposed PRV optimization algorithm reduces average leakage by 20.64%, surpassing the previous achievement of 16.07%. When applied to the South-Central and East zone WDS of Kapra Municipality, it identifies PRV locations that were previously missed by existing algorithms, resulting in average leakage reductions of 22.04% and 10.47%. These reductions translate to significant daily Water savings, enhancing Water supply reliability and reducing energy consumption. The second part of this research addresses the pressing issue of burst event detection and localization within the Water Distribution System. Burst events are a major contributor to Water losses and repair expenses. The study employs wireless sensor technology to monitor pressure and flow rate in real time, enabling the detection of pipeline abnormalities, particularly burst events. The methodology relies on transient analysis of pressure signals, utilizing Cumulative Sum and Wavelet analysis techniques to robustly identify burst occurrences. To enhance precision, burst event localization is achieved through meticulous analysis of time differentials in the arrival of negative pressure waveforms across distinct pressure sensing points, aided by nodal matrix analysis. To evaluate the effectiveness of this methodology, a PVC Water pipeline test bed is employed, demonstrating the algorithm's success in detecting pipeline burst events at flow rates of 2-3 l/s. Remarkably, the algorithm achieves a localization error of merely 3 meters, outperforming previously established algorithms. This research presents a significant advancement in efficient burst event detection and localization within Water pipelines, holding the potential to markedly curtail Water losses and the concomitant financial implications. In conclusion, this combined research addresses critical challenges in Water distribution networks, offering solutions for optimizing pressure control, pump speed, burst event detection, and localization. These findings contribute to the enhancement of Water Distribution System, resulting in improved Water supply reliability, reduced Water losses, and substantial cost savings. The integrated approach presented in this paper holds promise for municipalities and utilities seeking to improve the efficiency and sustainability of their Water distribution networks.Keywords: pressure reduce valve, complex networks, variable speed pump, wavelet transform, burst detection, CUSUM (Cumulative Sum), water pipeline monitoring
Procedia PDF Downloads 79267 Numerical Investigation of Hot Oil Velocity Effect on Force Heat Convection and Impact of Wind Velocity on Convection Heat Transfer in Receiver Tube of Parabolic Trough Collector System
Authors: O. Afshar
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A solar receiver is designed for operation under extremely uneven heat flux distribution, cyclic weather, and cloud transient cycle conditions, which can include large thermal stress and even receiver failure. In this study, the effect of different oil velocity on convection coefficient factor and impact of wind velocity on local Nusselt number by Finite Volume Method will be analyzed. This study is organized to give an overview of the numerical modeling using a MATLAB software, as an accurate, time efficient and economical way of analyzing the heat transfer trends over stationary receiver tube for different Reynolds number. The results reveal when oil velocity is below 0.33m/s, the value of convection coefficient is negligible at low temperature. The numerical graphs indicate that when oil velocity increases up to 1.2 m/s, heat convection coefficient increases significantly. In fact, a reduction in oil velocity causes a reduction in heat conduction through the glass envelope. In addition, the different local Nusselt number is reduced when the wind blows toward the concave side of the collector and it has a significant effect on heat losses reduction through the glass envelope.Keywords: receiver tube, heat convection, heat conduction, Nusselt number
Procedia PDF Downloads 350266 Investigating the Dynamics of Knowledge Acquisition in Learning Using Differential Equations
Authors: Gilbert Makanda, Roelf Sypkens
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A mathematical model for knowledge acquisition in teaching and learning is proposed. In this study we adopt the mathematical model that is normally used for disease modelling into teaching and learning. We derive mathematical conditions which facilitate knowledge acquisition. This study compares the effects of dropping out of the course at early stages with later stages of learning. The study also investigates effect of individual interaction and learning from other sources to facilitate learning. The study fits actual data to a general mathematical model using Matlab ODE45 and lsqnonlin to obtain a unique mathematical model that can be used to predict knowledge acquisition. The data used in this study was obtained from the tutorial test results for mathematics 2 students from the Central University of Technology, Free State, South Africa in the department of Mathematical and Physical Sciences. The study confirms already known results that increasing dropout rates and forgetting taught concepts reduce the population of knowledgeable students. Increasing teaching contacts and access to other learning materials facilitate knowledge acquisition. The effect of increasing dropout rates is more enhanced in the later stages of learning than earlier stages. The study opens up a new direction in further investigations in teaching and learning using differential equations.Keywords: differential equations, knowledge acquisition, least squares nonlinear, dynamical systems
Procedia PDF Downloads 358265 Motion Detection Method for Clutter Rejection in the Bio-Radar Signal Processing
Authors: Carolina Gouveia, José Vieira, Pedro Pinho
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The cardiopulmonary signal monitoring, without the usage of contact electrodes or any type of in-body sensors, has several applications such as sleeping monitoring and continuous monitoring of vital signals in bedridden patients. This system has also applications in the vehicular environment to monitor the driver, in order to avoid any possible accident in case of cardiac failure. Thus, the bio-radar system proposed in this paper, can measure vital signals accurately by using the Doppler effect principle that relates the received signal properties with the distance change between the radar antennas and the person’s chest-wall. Once the bio-radar aim is to monitor subjects in real-time and during long periods of time, it is impossible to guarantee the patient immobilization, hence their random motion will interfere in the acquired signals. In this paper, a mathematical model of the bio-radar is presented, as well as its simulation in MATLAB. The used algorithm for breath rate extraction is explained and a method for DC offsets removal based in a motion detection system is proposed. Furthermore, experimental tests were conducted with a view to prove that the unavoidable random motion can be used to estimate the DC offsets accurately and thus remove them successfully.Keywords: bio-signals, DC component, Doppler effect, ellipse fitting, radar, SDR
Procedia PDF Downloads 133264 An Analysis of the Temporal Aspects of Visual Attention Processing Using Rapid Series Visual Processing (RSVP) Data
Authors: Shreya Borthakur, Aastha Vartak
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This Electroencephalogram (EEG) project on Rapid Visual Serial Processing (RSVP) paradigm explores the temporal dynamics of visual attention processing in response to rapidly presented visual stimuli. The study builds upon previous research that used real-world images in RSVP tasks to understand the emergence of object representations in the human brain. The objectives of the research include investigating the differences in accuracy and reaction times between 5 Hz and 20 Hz presentation rates, as well as examining the prominent brain waves, particularly alpha and beta waves, associated with the attention task. The pre-processing and data analysis involves filtering EEG data, creating epochs for target stimuli, and conducting statistical tests using MATLAB, EEGLAB, Chronux toolboxes, and R. The results support the hypotheses, revealing higher accuracy at a slower presentation rate, faster reaction times for less complex targets, and the involvement of alpha and beta waves in attention and cognitive processing. This research sheds light on how short-term memory and cognitive control affect visual processing and could have practical implications in fields like education.Keywords: RSVP, attention, visual processing, attentional blink, EEG
Procedia PDF Downloads 64263 Atomic Absorption Spectroscopic Analysis of Heavy Metals in Cancerous Breast Tissues among Women in Jos, Nigeria
Authors: Opeyemi Peter Idowu
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Breast cancer is prevalent in northern Nigerian women, most especially in Jos, Plateau State, owing to anthropogenic activities such as solid earth mineral mining as far back as 1904. In this study, atomic absorption spectrometry was used to determine the concentration of eight heavy metals (Cd, As, Cr, Cu, Fe, Pb, Ni, and Zn) in cancerous and non-cancerous breast tissues of Jos Nigerian Women. The levels of heavy metals ranged from 1.08 to 29.34 mg/kg, 0.29 to 10.76 mg/kg, 0.35 to 51.93 mg/kg, 5.15 to 62.93 mg/kg, 11.64 to 51.10 mg/kg, 0.42 to 83.16 mg/kg, 2.08 to 43.07 mg/kg and 1.67 to 71.53 mg/kg for Cd, As, Cr, Cu, Fe, Pb, Ni and Zn respectively. Using MATLAB R2016a, significant differences (tᵥ = 0.0041 - 0.0317) existed between the levels of all the heavy metals in cancerous and non-cancerous breast tissues except Fe. At 0.01 level of significance, a positive significant correlation existed between Pb and Fe, Pb and Cu, Pb and Fe, Ni and Fe, Cr and Pb, as well as Ni and Cr (r = 0.583 – 0.998) in cancerous breast tissues. Using ANOVA, significant differences also occurred in the levels of these heavy metals in cancerous breast tissues (p = 1.910510×10⁻²⁶). The relatively high levels of the cancer-induced heavy metals (Cd, As, Cr, and Pb) compared with control indicated contamination or exposure to heavy metals, which could be the major cause of cancer in these female subjects. This was evidence of contamination as a result of exposure by ingestion, inhalation, or other means to one anthropogenic activity of the other. Therapeutic measures such as gastric lavage, ascorbic acid consumption, and divalent cation treatment are all effective ways to manage heavy metal toxicity in the subjects to lower the risk of breast cancer.Keywords: breast cancer, heavy metals, spectroscopy, bio-accumulation
Procedia PDF Downloads 8262 Radial Distribution Network Reliability Improvement by Using Imperialist Competitive Algorithm
Authors: Azim Khodadadi, Sahar Sadaat Vakili, Ebrahim Babaei
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This study presents a numerical method to optimize the failure rate and repair time of a typical radial distribution system. Failure rate and repair time are effective parameters in customer and energy based indices of reliability. Decrease of these parameters improves reliability indices. Thus, system stability will be boost. The penalty functions indirectly reflect the cost of investment which spent to improve these indices. Constraints on customer and energy based indices, i.e. SAIFI, SAIDI, CAIDI and AENS have been considered by using a new method which reduces optimization algorithm controlling parameters. Imperialist Competitive Algorithm (ICA) used as main optimization technique and particle swarm optimization (PSO), simulated annealing (SA) and differential evolution (DE) has been applied for further investigation. These algorithms have been implemented on a test system by MATLAB. Obtained results have been compared with each other. The optimized values of repair time and failure rate are much lower than current values which this achievement reduced investment cost and also ICA gives better answer than the other used algorithms.Keywords: imperialist competitive algorithm, failure rate, repair time, radial distribution network
Procedia PDF Downloads 658261 Power Quality Improvement Using UPQC Integrated with Distributed Generation Network
Authors: B. Gopal, Pannala Krishna Murthy, G. N. Sreenivas
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The increasing demand of electric power is giving an emphasis on the need for the maximum utilization of renewable energy sources. On the other hand maintaining power quality to satisfaction of utility is an essential requirement. In this paper the design aspects of a Unified Power Quality Conditioner integrated with photovoltaic system in a distributed generation is presented. The proposed system consist of series inverter, shunt inverter are connected back to back on the dc side and share a common dc-link capacitor with Distributed Generation through a boost converter. The primary task of UPQC is to minimize grid voltage and load current disturbances along with reactive and harmonic power compensation. In addition to primary tasks of UPQC, other functionalities such as compensation of voltage interruption and active power transfer to the load and grid in both islanding and interconnected mode have been addressed. The simulation model is design in MATLAB/ Simulation environment and the results are in good agreement with the published work.Keywords: distributed generation (DG), interconnected mode, islanding mode, maximum power point tracking (mppt), power quality (PQ), unified power quality conditioner (UPQC), photovoltaic array (PV)
Procedia PDF Downloads 500260 Fuzzy Adaptive Control of an Intelligent Hybrid HPS (Pvwindbat), Grid Power System Applied to a Dwelling
Authors: A. Derrouazin, N. Mekkakia-M, R. Taleb, M. Helaimi, A. Benbouali
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Nowadays the use of different sources of renewable energy for the production of electricity is the concern of everyone, as, even impersonal domestic use of the electricity in isolated sites or in town. As the conventional sources of energy are shrinking, a need has arisen to look for alternative sources of energy with more emphasis on its optimal use. This paper presents design of a sustainable Hybrid Power System (PV-Wind-Storage) assisted by grid as supplementary sources applied to case study residential house, to meet its entire energy demand. A Fuzzy control system model has been developed to optimize and control flow of power from these sources. This energy requirement is mainly fulfilled from PV and Wind energy stored in batteries module for critical load of a residential house and supplemented by grid for base and peak load. The system has been developed for maximum daily households load energy of 3kWh and can be scaled to any higher value as per requirement of individual /community house ranging from 3kWh/day to 10kWh/day, as per the requirement. The simulation work, using intelligent energy management, has resulted in an optimal yield leading to average reduction in cost of electricity by 50% per day.Keywords: photovoltaic (PV), wind turbine, battery, microcontroller, fuzzy control (FC), Matlab
Procedia PDF Downloads 643259 The Relationship between Spanish Economic Variables: Evidence from the Wavelet Techniques
Authors: Concepcion Gonzalez-Concepcion, Maria Candelaria Gil-Fariña, Celina Pestano-Gabino
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We analyze six relevant economic and financial variables for the period 2000M1-2015M3 in the context of the Spanish economy: a financial index (IBEX35), a commodity (Crude Oil Price in euros), a foreign exchange index (EUR/USD), a bond (Spanish 10-Year Bond), the Spanish National Debt and the Consumer Price Index. The goal of this paper is to analyze the main relations between them by computing the Wavelet Power Spectrum and the Cross Wavelet Coherency associated with Morlet wavelets. By using a special toolbox in MATLAB, we focus our interest on the period variable. We decompose the time-frequency effects and improve the interpretation of the results by non-expert users in the theory of wavelets. The empirical evidence shows certain instability periods and reveals various changes and breaks in the causality relationships for sample data. These variables were individually analyzed with Daubechies Wavelets to visualize high-frequency variance, seasonality, and trend. The results are included in Proceeding 20th International Academic Conference, 2015, International Institute of Social and Economic Sciences (IISES), Madrid.Keywords: economic and financial variables, Spain, time-frequency domain, wavelet coherency
Procedia PDF Downloads 233258 Grain Size Characteristics and Sediments Distribution in the Eastern Part of Lekki Lagoon
Authors: Mayowa Philips Ibitola, Abe Oluwaseun Banji, Olorunfemi Akinade-Solomon
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A total of 20 bottom sediment samples were collected from the Lekki Lagoon during the wet and dry season. The study was carried out to determine the textural characteristics, sediment distribution pattern and energy of transportation within the lagoon system. The sediment grain sizes and depth profiling was analyzed using dry sieving method and MATLAB algorithm for processing. The granulometric reveals fine grained sand both for the wet and dry season with an average mean value of 2.03 ϕ and -2.88 ϕ, respectively. Sediments were moderately sorted with an average inclusive standard deviation of 0.77 ϕ and -0.82 ϕ. Skewness varied from strongly coarse and near symmetrical 0.34- ϕ and 0.09 ϕ. The kurtosis average value was 0.87 ϕ and -1.4 ϕ (platykurtic and leptokurtic). Entirely, the bathymetry shows an average depth of 4.0 m. The deepest and shallowest area has a depth of 11.2 m and 0.5 m, respectively. High concentration of fine sand was observed at deep areas compared to the shallow areas during wet and dry season. Statistical parameter results show that the overall sediments are sorted, and deposited under low energy condition over a long distance. However, sediment distribution and sediment transport pattern of Lekki Lagoon is controlled by a low energy current and the down slope configuration of the bathymetry enhances the sorting and the deposition rate in the Lekki Lagoon.Keywords: Lekki Lagoon, Marine sediment, bathymetry, grain size distribution
Procedia PDF Downloads 228257 Lab Bench for Synthetic Aperture Radar Imaging System
Authors: Karthiyayini Nagarajan, P. V. Ramakrishna
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Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar (SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System (Lab Bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes.Keywords: synthetic aperture radar, radio reflection model, lab bench, imaging engineering
Procedia PDF Downloads 487256 Classification of Myoelectric Signals Using Multilayer Perceptron Neural Network with Back-Propagation Algorithm in a Wireless Surface Myoelectric Prosthesis of the Upper-Limb
Authors: Kevin D. Manalo, Jumelyn L. Torres, Noel B. Linsangan
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This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network.Keywords: field programmable gate array, multilayer perceptron neural network, verilog, zigbee
Procedia PDF Downloads 385255 Design and Implementation of a Lab Bench for Synthetic Aperture Radar Imaging System
Authors: Karthiyayini Nagarajan, P. V. RamaKrishna
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Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar(SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System(lab bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes.Keywords: synthetic aperture radar, radio reflection model, lab bench
Procedia PDF Downloads 461254 Obstacle Avoidance Using Image-Based Visual Servoing Based on Deep Reinforcement Learning
Authors: Tong He, Long Chen, Irag Mantegh, Wen-Fang Xie
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This paper proposes an image-based obstacle avoidance and tracking target identification strategy in GPS-degraded or GPS-denied environment for an Unmanned Aerial Vehicle (UAV). The traditional force algorithm for obstacle avoidance could produce local minima area, in which UAV cannot get away obstacle effectively. In order to eliminate it, an artificial potential approach based on harmonic potential is proposed to guide the UAV to avoid the obstacle by using the vision system. And image-based visual servoing scheme (IBVS) has been adopted to implement the proposed obstacle avoidance approach. In IBVS, the pixel accuracy is a key factor to realize the obstacle avoidance. In this paper, the deep reinforcement learning framework has been applied by reducing pixel errors through constant interaction between the environment and the agent. In addition, the combination of OpenTLD and Tensorflow based on neural network is used to identify the type of tracking target. Numerical simulation in Matlab and ROS GAZEBO show the satisfactory result in target identification and obstacle avoidance.Keywords: image-based visual servoing, obstacle avoidance, tracking target identification, deep reinforcement learning, artificial potential approach, neural network
Procedia PDF Downloads 134253 A Novel Multi-Objective Park and Ride Control Scheme Using Renewable Energy Sources: Cairo Case Study
Authors: Mohammed Elsayed Lotfy Elsayed Abouzeid, Tomonobu Senjyu
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A novel multi-objective park and ride control approach is presented in this research. Park and ride will encourage the owners of the vehicles to leave their cars in the nearest points (on the edges of the crowded cities) and use public transportation facilities (train, bus, metro, or mon-rail) to reach their work inside the crowded city. The proposed control scheme is used to design electric vehicle charging stations (EVCS) to charge 1000 electric vehicles (EV) during their owners' work time. Cairo, Egypt is used as a case study. Photovoltaic (PV) and battery energy storage system (BESS) are used to meet the EVCS demand. Two multi-objective optimization techniques (MOGA and epsilon-MOGA) are utilized to get the optimal sizes of PV and BESS so as to meet the load demand and minimize the total life cycle cost. Detailed analysis and comparison are held to investigate the performance of the proposed control scheme using MATLAB.Keywords: Battery Energy Storage System, Electric Vehicle, Park and Ride, Photovoltaic, Multi-objective
Procedia PDF Downloads 135252 Performance Evaluation of Clustered Routing Protocols for Heterogeneous Wireless Sensor Networks
Authors: Awatef Chniguir, Tarek Farah, Zouhair Ben Jemaa, Safya Belguith
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Optimal routing allows minimizing energy consumption in wireless sensor networks (WSN). Clustering has proven its effectiveness in organizing WSN by reducing channel contention and packet collision and enhancing network throughput under heavy load. Therefore, nowadays, with the emergence of the Internet of Things, heterogeneity is essential. Stable election protocol (SEP) that has increased the network stability period and lifetime is the first clustering protocol for heterogeneous WSN. SEP and its descendants, namely SEP, Threshold Sensitive SEP (TSEP), Enhanced TSEP (ETSSEP) and Current Energy Allotted TSEP (CEATSEP), were studied. These algorithms’ performance was evaluated based on different metrics, especially first node death (FND), to compare their stability. Simulations were conducted on the MATLAB tool considering two scenarios: The first one demonstrates the fraction variation of advanced nodes by setting the number of total nodes. The second considers the interpretation of the number of nodes while keeping the number of advanced nodes permanent. CEATSEP outperforms its antecedents by increasing stability and, at the same time, keeping a low throughput. It also operates very well in a large-scale network. Consequently, CEATSEP has a useful lifespan and energy efficiency compared to the other routing protocol for heterogeneous WSN.Keywords: clustering, heterogeneous, stability, scalability, IoT, WSN
Procedia PDF Downloads 126251 Supplemental VisCo-friction Damping for Dynamical Structural Systems
Authors: Sharad Singh, Ajay Kumar Sinha
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Coupled dampers like viscoelastic-frictional dampers for supplemental damping are a newer technique. In this paper, innovative Visco-frictional damping models have been presented and investigated. This paper attempts to couple frictional and fluid viscous dampers into a single unit of supplemental dampers. Visco-frictional damping model is developed by series and parallel coupling of frictional and fluid viscous dampers using Maxwell and Kelvin-Voigat models. The time analysis has been performed using numerical simulation on an SDOF system with varying fundamental periods, subject to a set of 12 ground motions. The simulation was performed using the direct time integration method. MATLAB programming tool was used to carry out the numerical simulation. The response behavior has been analyzed for the varying time period and added damping. This paper compares the response reduction behavior of the two modes of coupling. This paper highlights the performance efficiency of the suggested damping models. It also presents a mathematical modeling approach to visco-frictional dampers and simultaneously suggests the suitable mode of coupling between the two sub-units.Keywords: hysteretic damping, Kelvin model, Maxwell model, parallel coupling, series coupling, viscous damping
Procedia PDF Downloads 154250 Fuzzy Logic Based Ventilation for Controlling Harmful Gases in Livestock Houses
Authors: Nuri Caglayan, H. Kursat Celik
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There are many factors that influence the health and productivity of the animals in livestock production fields, including temperature, humidity, carbon dioxide (CO2), ammonia (NH3), hydrogen sulfide (H2S), physical activity and particulate matter. High NH3 concentrations reduce feed consumption and cause daily weight gain. At high concentrations, H2S causes respiratory problems and CO2 displace oxygen, which can cause suffocation or asphyxiation. Good air quality in livestock facilities can have an impact on the health and well-being of animals and humans. Air quality assessment basically depends on strictly given limits without taking into account specific local conditions between harmful gases and other meteorological factors. The stated limitations may be eliminated. using controlling systems based on neural networks and fuzzy logic. This paper describes a fuzzy logic based ventilation algorithm, which can calculate different fan speeds under pre-defined boundary conditions, for removing harmful gases from the production environment. In the paper, a fuzzy logic model has been developed based on a Mamedani’s fuzzy method. The model has been built on MATLAB software. As the result, optimum fan speeds under pre-defined boundary conditions have been presented.Keywords: air quality, fuzzy logic model, livestock housing, fan speed
Procedia PDF Downloads 366249 Automatic Generating CNC-Code for Milling Machine
Authors: Chalakorn Chitsaart, Suchada Rianmora, Mann Rattana-Areeyagon, Wutichai Namjaiprasert
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G-code is the main factor in computer numerical control (CNC) machine for controlling the tool-paths and generating the profile of the object’s features. For obtaining high surface accuracy of the surface finish, non-stop operation is required for CNC machine. Recently, to design a new product, the strategy that concerns about a change that has low impact on business and does not consume lot of resources has been introduced. Cost and time for designing minor changes can be reduced since the traditional geometric details of the existing models are applied. In order to support this strategy as the alternative channel for machining operation, this research proposes the automatic generating codes for CNC milling operation. Using this technique can assist the manufacturer to easily change the size and the geometric shape of the product during the operation where the time spent for setting up or processing the machine are reduced. The algorithm implemented on MATLAB platform is developed by analyzing and evaluating the geometric information of the part. Codes are created rapidly to control the operations of the machine. Comparing to the codes obtained from CAM, this developed algorithm can shortly generate and simulate the cutting profile of the part.Keywords: geometric shapes, milling operation, minor changes, CNC Machine, G-code, cutting parameters
Procedia PDF Downloads 345248 Experimental and Theoretical Analysis of the Electromagnetic Environment in the Vicinity of Two 220Kv Power Lines
Authors: Wafa Tourab, Abdessalem Babouri, Mohamed Nemamcha
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This work presents an experimental and theoretical characterization of electromagnetic environment in the vicinity of EL-HADJAR high voltage substation located in the eastern Algerian within a very high populated zone. There have been analyses on the effects of electromagnetic fields emanating from coupled multi-lines power systems on the health of the workers and people living in proximity of substations. An experimental investigation has been conducted around a circuit of two 220Kv lines running in parallel. The experimental results are validated by a flexible code of calculus developed in the environment Matlab. The implications of the results are discussed and are in very good agreement with the ICNIRP reference levels for occupational and non-occupational exposures. In a case of study, the separation between the two structures “S” is varied to demonstrate its influence on the electric and magnetic charges quantities generated by the circuit of lines proposed. It is found that increasing S decreases the electric and magnetic fields which occur at the center of the structure then reduces the coupling between lines. We concluded that the evaluation of the spacing between the phase conductors is of paramount interest in the preparation of the line’s implantation inside the electrical posts to reduce them radiations in the environment.Keywords: low frequency, electromagnetic fields, electromagnetic coupling, high voltage power lines
Procedia PDF Downloads 385247 Multi-Tooled Robotic Hand for Tele-Operation of Explosive Devices
Authors: Faik Derya Ince, Ugur Topgul, Alp Gunay, Can Bayoglu, Dante J. Dorantes-Gonzalez
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Explosive attacks are arguably the most lethal threat that may occur in terrorist attacks. In order to counteract this issue, explosive ordnance disposal operators put their lives on the line to dispose of a possible improvised explosive device. Robots can make the disposal process more accurately and saving human lives. For this purpose, there is a demand for more accurate and dexterous manipulating robotic hands that can be teleoperated from a distance. The aim of this project is to design a robotic hand that contains two active and two passive DOF for each finger, as well as a minimum set of tools for mechanical cutting and screw driving within the same robotic hand. Both hand and toolset, are teleoperated from a distance from a haptic robotic glove in order to manipulate dangerous objects such as improvised explosive devices. SolidWorks® Computer-Aided Design, computerized dynamic simulation, and MATLAB® kinematic and static analysis were used for the robotic hand and toolset design. Novel, dexterous and robust solutions for the fingers were obtained, and six servo motors are used in total to remotely control the multi-tooled robotic hand. This project is still undergoing and presents currents results. Future research steps are also presented.Keywords: Explosive Manipulation, Robotic Hand, Tele-Operation, Tool Integration
Procedia PDF Downloads 133246 Modeling of a UAV Longitudinal Dynamics through System Identification Technique
Authors: Asadullah I. Qazi, Mansoor Ahsan, Zahir Ashraf, Uzair Ahmad
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System identification of an Unmanned Aerial Vehicle (UAV), to acquire its mathematical model, is a significant step in the process of aircraft flight automation. The need for reliable mathematical model is an established requirement for autopilot design, flight simulator development, aircraft performance appraisal, analysis of aircraft modifications, preflight testing of prototype aircraft and investigation of fatigue life and stress distribution etc. This research is aimed at system identification of a fixed wing UAV by means of specifically designed flight experiment. The purposely designed flight maneuvers were performed on the UAV and aircraft states were recorded during these flights. Acquired data were preprocessed for noise filtering and bias removal followed by parameter estimation of longitudinal dynamics transfer functions using MATLAB system identification toolbox. Black box identification based transfer function models, in response to elevator and throttle inputs, were estimated using least square error technique. The identification results show a high confidence level and goodness of fit between the estimated model and actual aircraft response.Keywords: fixed wing UAV, system identification, black box modeling, longitudinal dynamics, least square error
Procedia PDF Downloads 320245 Reducing Support Structures in Design for Additive Manufacturing: A Neural Networks Approach
Authors: Olivia Borgue, Massimo Panarotto, Ola Isaksson
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This article presents a neural networks-based strategy for reducing the need for support structures when designing for additive manufacturing (AM). Additive manufacturing is a relatively new and immature industrial technology, and the information to make confident decisions when designing for AM is limited. This lack of information impacts especially the early stages of engineering design, for instance, it is difficult to actively consider the support structures needed for manufacturing a part. This difficulty is related to the challenge of designing a product geometry accounting for customer requirements, manufacturing constraints and minimization of support structure. The approach presented in this article proposes an automatized geometry modification technique for reducing the use of the support structures while designing for AM. This strategy starts with a neural network-based strategy for shape recognition to achieve product classification, using an STL file of the product as input. Based on the classification, an automatic part geometry modification based on MATLAB© is implemented. At the end of the process, the strategy presents different geometry modification alternatives depending on the type of product to be designed. The geometry alternatives are then evaluated adopting a QFD-like decision support tool.Keywords: additive manufacturing, engineering design, geometry modification optimization, neural networks
Procedia PDF Downloads 246244 Profitability Assessment of Granite Aggregate Production and the Development of a Profit Assessment Model
Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David
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The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an artificial neural network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R²), Root mean square error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.Keywords: national development, granite, profitability assessment, ANN models
Procedia PDF Downloads 91243 Performance Analysis of PAPR Reduction in OFDM Systems based on Partial Transmit Sequence (PTS) Technique
Authors: Alcardo Alex Barakabitze, Tan Xiaoheng
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Orthogonal Frequency Division Multiplexing (OFDM) is a special case of Multi-Carrier Modulation (MCM) technique which transmits a stream of data over a number of lower data rate subcarriers. OFDM splits the total transmission bandwidth into a number of orthogonal and non-overlapping subcarriers and transmit the collection of bits called symbols in parallel using these subcarriers. This paper explores the Peak to Average Power Reduction (PAPR) using the Partial Transmit Sequence technique. We provide the distribution analysis and the basics of OFDM signals and then show how the PAPR increases as the number of subcarriers increases. We provide the performance analysis of CCDF and PAPR expressed in decibels through MATLAB simulations. The simulation results show that, in PTS technique, the performance of PAPR reduction in OFDM systems improves significantly as the number of sub-blocks increases. However, by keeping the same number of sub-blocks variation, oversampling factor and the number of OFDM blocks’ iteration for generating the CCDF, the OFDM systems with 128 subcarriers have an improved performance in PAPR reduction compared to OFDM systems with 256, 512 or >512 subcarriers.Keywords: OFDM, peak to average power reduction (PAPR), bit error rate (BER), subcarriers, wireless communications
Procedia PDF Downloads 507242 Energy Management System with Temperature Rise Prevention on Hybrid Ships
Authors: Asser S. Abdelwahab, Nabil H. Abbasy, Ragi A. Hamdy
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Marine shipping has now become one of the major worldwide contributors to pollution and greenhouse gas emissions. Hybrid ships technology based on multiple energy sources has taken a great scope of research to get rid of ship emissions and cut down fuel expenses. Insufficiency between power generated and the demand load to withstand the transient behavior on ships during severe climate conditions will lead to a blackout. Thus, an efficient energy management system (EMS) is a mandatory scope for achieving higher system efficiency while enhancing the lifetime of the onboard storage systems is another salient EMS scope. Considering energy storage system conditions, both the battery state of charge (SOC) and temperature represent important parameters to prevent any malfunction of the storage system that eventually degrades the whole system. In this paper, a two battery packs ratio fuzzy logic control model is proposed. The overall aim is to control the charging/discharging current while including both the battery SOC and temperature in the energy management system. The full designs of the proposed controllers are described and simulated using Matlab. The results prove the successfulness of the proposed controller in stabilizing the system voltage during both loading and unloading while keeping the energy storage system in a healthy condition.Keywords: energy storage system, power shipboard, hybrid ship, thermal runaway
Procedia PDF Downloads 193241 Performance Analysis of Microelectromechanical Systems-Based Piezoelectric Energy Harvester
Authors: Sanket S. Jugade, Swapneel U. Naphade, Satyabodh M. Kulkarni
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Microscale energy harvesters can be used to convert ambient mechanical vibrations to electrical energy. Such devices have great applications in low powered electronics in remote environments like powering wireless sensor nodes of Internet of Things, lightings on highways or in ships, etc. In this paper, a Microelectromechanical systems (MEMS) based energy harvester has been modeled using Analytical and Finite Element Method (FEM). The device consists of a microcantilever with a proof mass attached to its free end and a Polyvinylidene Fluoride (PVDF) piezoelectric thin film deposited on the surface of microcantilever in a unimorph or bimorph configuration. For the analytical method, the energy harvester was modeled as an equivalent electrical system in SIMULINK. The Finite element model was developed and analyzed using the commercial package COMSOL Multiphysics. The modal analysis was performed first to find the fundamental natural frequency and its variation with geometrical parameters of the system. Then the harmonic analysis was performed to find the input mechanical power, output electrical voltage, and power for a range of excitation frequencies and base acceleration values. The variation of output power with load resistance, PVDF film thickness, and damping values was also found out. The results from FEM were then validated with that of the analytical model. Finally, the performance of the device was optimized with respect to various electro-mechanical parameters. For a unimorph configuration consisting of single crystal silicon microcantilever of dimensions 8mm×2mm×80µm and proof mass of 9.32 mg with optimal values of the thickness of PVDF film and load resistance as 225 µm and 20 MΩ respectively, the maximum electrical power generated for base excitation of 0.2g at 630 Hz is 0.9 µW.Keywords: bimorph, energy harvester, FEM, harmonic analysis, MEMS, PVDF, unimorph
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